OpenCV 标定和畸变校正(2)

海尔630冰箱的标定和畸变校正。上篇文章中直接使用OpenCV的例程进行畸变校正的效果不太理想。使用以下方法（张正友）效果更好。

京东链接：

http://item.jd.com/4027713.html#crumb-wrap

标定代码：

/************************************************************************运行环境：VS2013+OpenCV 2.4.13运行结果：检测拍摄的棋盘畸变图像，获取摄像头的畸变信息*************************************************************************/#include <opencv2\opencv.hpp>#include <fstream>#include <iostream>using namespace std;using namespace cv;char dir[64];char fileNames[64];char chess_boardImage_path[] = "E:\\hanxiaoxuan\\distort\\";char chess_boardDetect_path[] = "E:\\hanxiaoxuan\\distort_detect\\";char chess_boardCorner_path[] = "E:\\hanxiaoxuan\\distort_corner\\";char calibrationResult[] = "E:\\hanxiaoxuan\\calibration_result.txt";char datFileName[] = "E:\\hanxiaoxuan\\camParam.dat";int main(){string *imageList = new string[100]; string *chess_boardList = new string[100];  ofstream fout(calibrationResult);  //保存标定结果的文件// 利用dir命令将当前目录下的.jpg文件名写入names.txtsprintf(dir, "%s%s%s%s%s%s", "dir ", chess_boardImage_path, "*.jpg", " /a /b >", chess_boardImage_path, "names.txt");system(dir);char name[64] = "";// 打开文件读取其中的文件名sprintf(fileNames, "%s%s", chess_boardImage_path, "names.txt");FILE* fp = fopen(fileNames, "r");if (NULL == fp)printf("error,cannot open the name list");// 获得文件数量int line = 0;while (fgets(name, 64, fp) != NULL){char subname[64];sscanf(name, "%[^\n]%s", subname);string image_name;stringstream stream;stream << subname;image_name = stream.str();imageList[line] = image_name.substr(0, image_name.length() - 4);line++;}//读取每一幅图像，从中提取出角点，然后对角点进行亚像素精确化cout << "开始提取角点………………" << endl;int image_count = line;                    //图像数量Size board_size = Size(9, 6);            //棋盘上每行、列的内角点数vector<Point2f> corners;                  //缓存每幅图像上检测到的角点vector<vector<Point2f>>  corners_Seq;    //保存检测到的所有角点vector<Mat>  image_Seq;int successImageNum = 0;//成功提取角点的棋盘图数量int count = 0;for (int i = 0; i != image_count; i++){cout << "Frame #" << i + 1 << "..." << endl;string imageFileName;imageFileName = imageList[i];     //图像的文件名imageFileName += ".jpg";       //图像的文件名.jpgcv::Mat image = imread(chess_boardImage_path + imageFileName);//提取角点cv::Mat imageGray;cvtColor(image, imageGray, CV_RGB2GRAY);bool patternfound = findChessboardCorners(image, board_size, corners, CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE +CALIB_CB_FAST_CHECK);if (!patternfound){cout << "can not find chessboard corners!\n";continue;exit(1);}else{//亚像素精确化cornerSubPix(imageGray, corners, Size(11, 11), Size(-1, -1), TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));//绘制检测到的角点并保存Mat imageTemp = image.clone();for (int j = 0; j < corners.size(); j++){circle(imageTemp, corners[j], 10, Scalar(0, 0, 255), 2, 8, 0);}string imageFileName;imageFileName = imageList[i];imageFileName += "_corner.jpg";imwrite(chess_boardCorner_path + imageFileName, imageTemp);cout << "Frame corner#" << i + 1 << "...end" << endl;imwrite(chess_boardDetect_path + imageFileName, image);chess_boardList[successImageNum] = imageList[i];count = count + corners.size();successImageNum = successImageNum + 1;corners_Seq.push_back(corners);}image_Seq.push_back(image);}cout << "角点提取完成！\n";//摄像机标定cout << "开始标定………………" << endl;Size square_size = Size(30, 30);vector<vector<Point3f>>  object_Points;        //保存标定板上角点的三维坐标Mat image_points = Mat(1, count, CV_32FC2, Scalar::all(0));  //保存提取的所有角点vector<int>  point_counts;//初始化标定板上角点的三维坐标for (int t = 0; t < successImageNum; t++){vector<Point3f> tempPointSet;for (int i = 0; i < board_size.height; i++){for (int j = 0; j < board_size.width; j++){//假设标定板放在世界坐标系中z=0的平面上Point3f tempPoint;tempPoint.x = i*square_size.width;tempPoint.y = j*square_size.height;tempPoint.z = 0;tempPointSet.push_back(tempPoint);}}object_Points.push_back(tempPointSet);}for (int i = 0; i < successImageNum; i++){point_counts.push_back(board_size.width*board_size.height);}//开始标定Size image_size = image_Seq[0].size();cv::Matx33d intrinsic_matrix;    //摄像机内参数矩阵cv::Vec4d distortion_coeffs;     //摄像机的4个畸变系数：k1,k2,k3,k4std::vector<cv::Vec3d> rotation_vectors;                           //每幅图像的旋转向量std::vector<cv::Vec3d> translation_vectors;                        //每幅图像的平移向量int flags = 0;flags |= cv::fisheye::CALIB_RECOMPUTE_EXTRINSIC;flags |= cv::fisheye::CALIB_CHECK_COND;flags |= cv::fisheye::CALIB_FIX_SKEW;fisheye::calibrate(object_Points, corners_Seq, image_size, intrinsic_matrix, distortion_coeffs, rotation_vectors, translation_vectors, flags, cv::TermCriteria(3, 20, 1e-6));cout << "标定完成！\n";FILE *camParam = fopen(datFileName, "wb");if (camParam == NULL) {std::cout << "can not create data file: " << datFileName << " !!!" << std::endl;return false;}fwrite(&intrinsic_matrix, sizeof(cv::Matx33d), 1, camParam);fwrite(&distortion_coeffs, sizeof(cv::Vec4d), 1, camParam);fwrite(&image_size, sizeof(Size), 1, camParam);fclose(camParam);//对标定结果进行评价cout << "开始评价标定结果………………" << endl;double total_err = 0.0;                   //所有图像的平均误差的总和double err = 0.0;                        //每幅图像的平均误差vector<Point2f>  image_points2;             //保存重新计算得到的投影点cout << "每幅图像的标定误差：" << endl;cout << "每幅图像的标定误差：" << endl << endl;for (int i = 0; i < successImageNum; i++){vector<Point3f> tempPointSet = object_Points[i];//通过得到的摄像机内外参数，对空间的三维点进行重新投影计算，得到新的投影点fisheye::projectPoints(tempPointSet, image_points2, rotation_vectors[i], translation_vectors[i], intrinsic_matrix, distortion_coeffs);//计算新的投影点和旧的投影点之间的误差vector<Point2f> tempImagePoint = corners_Seq[i];Mat tempImagePointMat = Mat(1, tempImagePoint.size(), CV_32FC2);Mat image_points2Mat = Mat(1, image_points2.size(), CV_32FC2);for (size_t i = 0; i != tempImagePoint.size(); i++){image_points2Mat.at<Vec2f>(0, i) = Vec2f(image_points2[i].x, image_points2[i].y);tempImagePointMat.at<Vec2f>(0, i) = Vec2f(tempImagePoint[i].x, tempImagePoint[i].y);}err = norm(image_points2Mat, tempImagePointMat, NORM_L2);total_err += err /= point_counts[i];cout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;fout << "第" << i + 1 << "幅图像的平均误差：" << err << "像素" << endl;}cout << "总体平均误差：" << total_err / image_count << "像素" << endl;fout << "总体平均误差：" << total_err / image_count << "像素" << endl << endl;cout << "评价完成！" << endl;//保存标定结果cout << "开始保存标定结果………………" << endl;Mat rotation_matrix = Mat(3, 3, CV_32FC1, Scalar::all(0)); //保存每幅图像的旋转矩阵fout << "相机内参数矩阵：" << endl;fout << intrinsic_matrix << endl;fout << "畸变系数：\n";fout << distortion_coeffs << endl;for (int i = 0; i < successImageNum; i++){fout << "第" << i + 1 << "幅图像的旋转向量：" << endl;fout << rotation_vectors[i] << endl;//将旋转向量转换为相对应的旋转矩阵Rodrigues(rotation_vectors[i], rotation_matrix);fout << "第" << i + 1 << "幅图像的旋转矩阵：" << endl;fout << rotation_matrix << endl;fout << "第" << i + 1 << "幅图像的平移向量：" << endl;fout << translation_vectors[i] << endl;}cout << "完成保存" << endl;fout << endl;//显示标定结果Mat mapx = Mat(image_size, CV_32FC1);Mat mapy = Mat(image_size, CV_32FC1);Mat R = Mat::eye(3, 3, CV_32F);cout << "保存矫正图像" << endl;for (int i = 0; i != successImageNum; i++){cout << "Frame #" << i + 1 << "..." << endl;Mat newCameraMatrix = Mat(3, 3, CV_32FC1, Scalar::all(0));fisheye::initUndistortRectifyMap(intrinsic_matrix, distortion_coeffs, R, intrinsic_matrix, image_size, CV_32FC1, mapx, mapy);Mat t = image_Seq[i].clone();cv::remap(image_Seq[i], t, mapx, mapy, INTER_LINEAR);string imageFileName;imageFileName = chess_boardList[i];imageFileName += "_d.jpg";imwrite(chess_boardCorner_path + imageFileName, t);}cout << "保存结束" << endl;delete [] imageList;delete [] chess_boardList;return 0;}

得到两组参数，分别是相机的内参矩阵和畸变系数。保存在camPara.dat供calibration代码校正使用。

校正代码：

/************************************************************************运行环境：VS2013+OpenCV 2.4.13运行结果：根据摄像头的畸变信息，进行相机标定*************************************************************************/#include <opencv2\opencv.hpp>#include <fstream>#include <iostream>using namespace std;using namespace cv;char dir[64];char test_fileNames[64];char testFile_path[] = "E:\\hanxiaoxuan\\distort\\";char outputFile_path[] = "E:\\hanxiaoxuan\\undistort\\";char test_datFileName[] = "E:\\hanxiaoxuan\\camParam.dat";//string test_imageList[100];int main(){string *test_imageList = new string[100];  // 利用dir命令将当前目录下的.jpg文件名写入test_names.txtsprintf(dir, "%s%s%s%s%s%s", "dir ", testFile_path, "*.jpg", " /a /b >", testFile_path, "test_names.txt");system(dir);char test_name[64] = "";// 打开文件读取其中的文件名sprintf(test_fileNames, "%s%s", testFile_path, "test_names.txt");FILE* fp_test = fopen(test_fileNames, "r");if (NULL == fp_test)printf("error,cannot open the name list");// 获得文件数量int line = 0;while (fgets(test_name, 64, fp_test) != NULL){char subname[64];sscanf(test_name, "%[^\n]%s", subname);string image_name;stringstream stream;stream << subname;image_name = stream.str();test_imageList[line] = image_name.substr(0, image_name.length() - 4);line++;}string testName;//利用摄像机畸变参数对图片进行矫正cout << "保存矫正图像" << endl;for (int i = 0; i < line; i++){cout << "Frame #" << i + 1 << "..." << endl;testName = test_imageList[i] + ".jpg";Mat testImage = imread(testFile_path + testName);cv::Matx33d test_intrinsic_matrix;cv::Vec4d test_distortion_coeffs;Size test_image_size = testImage.size();FILE *test_camParam = fopen(test_datFileName, "rb");if (test_camParam == NULL) {std::cout << "can not create data file: " << test_datFileName << " !!!" << std::endl;return false;}fread(&test_intrinsic_matrix, sizeof(cv::Matx33d), 1, test_camParam);fread(&test_distortion_coeffs, sizeof(cv::Vec4d), 1, test_camParam);fread(&test_image_size, sizeof(Size), 1, test_camParam);fclose(test_camParam);Mat test_mapx = Mat(test_image_size, CV_32FC1);Mat test_mapy = Mat(test_image_size, CV_32FC1);Mat test_R = Mat::eye(3, 3, CV_32F);fisheye::initUndistortRectifyMap(test_intrinsic_matrix, test_distortion_coeffs, test_R, test_intrinsic_matrix, test_image_size, CV_32FC1, test_mapx, test_mapy);Mat t = testImage.clone();cv::remap(testImage, t, test_mapx, test_mapy, INTER_LINEAR);imwrite(outputFile_path + testName, t);}cout << "标定结束" << endl;delete [] test_imageList;return 0;}

校正效果如下：

原图1

原图校正后

原图2

校正后

校正效果基本令人满意。